Outlier Detection over Data Set Using Cluster-Based and Distance-Based Approach

Outlier detection is currently very active area of research in data set mining community. Finding outliers in a collection of patterns is a very well-known problem in the data mining field. An outlier is a pattern which is dissimilar with respect to the rest of the patterns in the dataset. Proposed Method for outlier detection uses hybrid approach. Purpose of approach is first to apply clustering algorithm that is kmeans which partition the dataset into number of clusters and then find outliers from the each resulting clusters using distance based method. The principle of outliers finding depend on the threshold. Threshold is set by user. The main objective of the second stage is a finding out the objects, which are far away from their cluster centroids. In proposed approach, two techniques are combining to efficiently find the outlier from the data set. The experimental results using real dataset demonstrate that proposed method takes less computational cost and performs better than the distance based method. Proposed algorithm efficiently prunes of the safe cells (inliers) and save huge number of extra calculations. Data mining is a process of extracting hidden and useful information from the data and the knowledge discovered by data mining is previously unknown, potentially useful, and valid and of high quality. Finding outliers is an important task in data mining. Outlier detection as a branch of data mining has many important applications and deserves more attention from data mining community. In recent years, conventional database querying methods are inadequate to extract useful information, and hence researches nowadays are focused to develop new techniques to meet the raised requirements. It is to be noted that the increase in dimensionality of data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of attributes. Outlier detection is an important research problem that aims to find objects that are considerably dissimilar, exceptional and inconsistent in the database. Medical application is a high dimensional domain hence determining outliers is found to be very tedious due to the Curse of dimensionality. There are various origins of outliers. With the growth of the medical dataset day by day, the process of determining outliers becomes more complex and tedious. Efficient detection of outliers reduces the risk of making poor decisions based on erroneous data, and aids in identifying, preventing, and repairing the effects of malicious or faulty behavior. Additionally, many data mining and